Asset Framework

Asset Framework is the plant contextualization layer within the DataHubX Suite. It creates a structured digital representation of the physical plant across companies, sites, plants, process units, equipment, and tags. By connecting operational data, engineering metadata, and technical documents with the assets they represent, Asset Framework allows engineers and AI applications to understand information within its correct operating context. It creates reusable asset models that support consistent monitoring and analysis across equipment, units, and plants.

Salient Features

Plant Hierarchy Modeling

Create a structured hierarchy covering companies, sites, plants, process units, equipment, and individual tags.

Tag-to-Asset Mapping

Connect raw and aggregated time-series tags with the equipment and operating context they represent.

Equipment Templates

Develop reusable templates for equipment such as pumps, compressors, boilers, reactors, and turbines.

Tag Categorization

Classify tags according to process, utility, quality, and safety applications for easier organization and discovery.

Tag Family Grouping

Engineering Metadata Management

Store technical specifications, operating attributes, engineering units, and other asset-level information.

Running Status Modeling

Configure operational states such as online, ramp-up, ramp-down, and offline to provide accurate context for asset analysis.

Document-to-Asset Mapping

Connect SOPs, manuals, P&IDs, safety documents, calibration records, and technical reports with the relevant assets.

Customizable Limits, Priorities, etc.

a. Engineers can manipulate configured KPI limits based on plant priorities.

Sensitivity Analysis

Optimax provides feature to run multiple cases simultaneously with different limit ranges to enhance the actionable insights.

Role-Based Access and Security

Provides access control to ensure only authorized personnel can view or edit monitoring configurations.

Result Analysis Tool

Offers diagnostic tools to help engineers quickly investigate and resolve performance issues with different configurations

Client Benefits

40–60% Faster Document Retrieval

Connect engineering and maintenance documents directly with relevant assets to reduce the time teams spend locating critical information.

Improve Asset-Level Visibility

Give teams a unified understanding of asset performance, operational status, KPIs, notifications, and supporting information.

Accelerate Asset Configuration

Reuse standardized equipment templates when adding similar assets, process units, or plants.

Reduce Manual Tag Mapping

Maintain tag relationships within a structured framework instead of repeatedly mapping information through spreadsheets.

Standardize Assets Across Plants

Apply consistent equipment models, metadata, and asset definitions across sites.

Improve Knowledge Retention

Keep technical documents and engineering knowledge connected to the assets they support, reducing dependence on individual employees.

Strengthen Contextual Analytics

Help monitoring and AI applications interpret plant information according to the relevant asset, process, and operating state.

Dashboard & Snapshots

Configure all tags related to specific Equipment:

Solution configuration [Defining the Objective: Maximize Production, Minimize Utility]:

Solution configuration [Defining cases of interest – Multiple cases can also be added]:

Result Tag Configuration: Identify the potential actionable insights.

Sensitivity Analysis: Supporting engineers to evaluate appropriate configurations

Case Studies

Development of Soft Sensor to predict the C5 contents in Debutaniser column Overhead:

Background

In modern petrochemical operations, maintaining product specifications within tight limits is critical for both quality assurance and process efficiency. One of the key challenges faced by a leading chemical plant was the need for real-time prediction and monitoring of C5 content in the overhead stream of the debutanizer column — a crucial step to ensure product specification compliance and optimize operations.

Challenge

Traditional laboratory testing methods for measuring C5 content introduced significant time delays, making real-time adjustments difficult. The plant required a fast, accurate, and reliable solution to continuously monitor C5 levels, allowing operators to take timely corrective actions and avoid off-spec products.

Solution with AnalyticX

Using AnalyticX, the engineering team was able to develop a soft sensor — a machine learning-based predictive model — specifically designed to estimate the C5 content in the debutanizer overhead stream in real time.

Key steps included:

Seamless ingestion of historical operational data into AnalyticX without any coding requirements.
Applying statistical analysis to identify key process variables influencing C5 concentrations.
Rapid development and training of a machine learning model tailored to predict C5 content with high accuracy.
Deployment of the predictive model into live operations for real-time product spec monitoring.

Results

Enhanced Product Monitoring:
Operators now have a real-time view of the predicted C5 content, significantly reducing reliance on delayed lab measurements.
Improved Process Control:
By closely tracking C5 levels, the plant was able to make timely adjustments to operational parameters, ensuring consistent product quality.
Reduced Off-Spec Production:
Real-time insights helped minimize off-spec batches, leading to material savings and reduced reprocessing costs.

Key Takeaways

AnalyticX enabled the rapid development of a soft sensor customized for critical KPI monitoring.

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